Development of accreditation modules based on hospital types in Iran: Protocol for a mixed methods study

BACKGROUND

Among different tools, accreditation is widely used worldwide to improve the quality and safety of hospital services. In Iran, as in many other countries, the same accreditation standards apply to all hospitals, regardless of their size and type of activity. This has given rise to many problems for hospitals.

MATERIALS AND METHODS

We will conduct this study in three phases: In the first phase, relevant individuals are interviewed to identify challenges caused to hospitals by applying the same standards for all types of hospitals and clarify issues that could be removed or changed in small hospitals. In the second phase, a scoping review is conducted on the literature about accreditation models worldwide. The first and second phases are conducted simultaneously, and a new accreditation model for Iran hospitals is derived by combining their results. In the final phase, using the Delphi technique, the obtained model and accreditation modules are verified during Delphi rounds.

DISCUSSION

A more appropriate accreditation model that matches the characteristics of the target hospitals could be the output of this study. It is expected that the model could improve the process of evaluating the quality of hospital services through the accreditation tool.

Do hospitals need to establish multiple hospital districts? A hospital-based perspective on the benefits of scale

Background

During the fight against COVID-19, China's public hospitals played the main role in taking on the most urgent, dangerous and arduous medical treatment and work. Therefore, in order to promote the high-quality development of hospitals, it is necessary to support some potential public hospitals to build and develop a "One Hospital with Multiple Campuses System" (OHMC) based on controlling the size of single hospitals, and to quickly convert their functions in the event of a severe epidemic.

Methods

The Cobb-Douglas production function and log-transformed production function were used to measure the appropriate hospital size for 22 public hospitals in a region of China.

Results

The eight OHMC hospitals that planned to be build are basically qualified to handle the conditions and potential of multi-districts from the perspective of economy of scale. The OHMC hospitals in operation appear to have weakened incremental scale rewards, because they are in the process of development, but they are still higher than the overall level of single-campus hospitals.

Conclusion

The expansion of hospital scale may bring the advantages of group development, but it may also bring about problems including rising hospital cost, increasing management and operation cost, inefficient allocation of medical resources and unbalanced development.

New evidence on geographic disparities in United States hospital capacity

OBJECTIVE

To characterize the quantity and quality of hospital capacity across the United States.

DATA SOURCES

We combine a 2017 near-census of US hospital inpatient discharges from the Healthcare Cost and Utilization Project (HCUP) with American Hospital Association Survey, Hospital Compare, and American Community Survey data.

STUDY DESIGN

This study produces local hospital capacity quantity and care quality measures by allocating capacity to zip codes using market shares and population totals. Disparities in these measures are examined by race and ethnicity, income, age, and urbanicity.

DATA COLLECTION/EXTRACTION METHODS

All data are derived from pre-existing sources. All hospitals and zip codes in states, including the District of Columbia, contributing complete data to HCUP in 2017 are included.

PRINCIPAL FINDINGS

Non-Hispanic Black individuals living in zip codes supplied, on average, 0.11 more beds per 1000 population (SE = 0.01) than places where non-Hispanic White individuals live. However, the hospitals supplying this capacity have 0.36 fewer staff per bed (SE = 0.03) and perform worse on many care quality measures. Zip codes in the most urban parts of America have the least hospital capacity (2.11 beds per 1000 persons; SEM = 0.01) from across the rural-urban continuum. While more rural areas have markedly higher capacity levels, urban areas have advantages in staff and capital per bed. We do not find systematic differences in care quality between rural and urban areas.

CONCLUSIONS

This study highlights the importance of lower hospital care quality and resource intensity in driving racial and ethnic, as well as income, disparities in hospital care-related outcomes. This study also contributes an alternative approach for measuring local hospital capacity that accounts for cross-hospital service area flows. Adjusting for these flows is necessary to avoid underestimating the supply of capacity in rural areas and overestimating it in places where non-Hispanic Black individuals tend to live.

Changes in the Number of Physicians and Hospital Bed Capacity in Europe

OBJECTIVES

Our aim was to examine the numbers of practicing physicians and total numbers of hospital beds in European Organisation for Economic Co-operation and Development countries.

METHODS

Data analyzed were derived from the "Organisation for Economic Co-operation and Development Health Statistics 2020" database between 1980 and 2018. The selected countries were compared according to the type of healthcare system and geographical location by parametric and nonparametric tests.

RESULTS

In 1980, Bismarck-type systems showed an average number of physicians of 2.3 persons/1000 population; in Beveridge-type systems, it was 1.7 persons. By 2018, it leveled out reaching 3.9 persons in both healthcare system types. In 1980, average physician number/1000 was 2.5 persons in Eastern Europe; in Western Europe, it was 1.9 persons. By 2018 this proportion changed with Western Europe having the higher number (3.7 persons; 3.9 persons). In 1980, average number of hospital beds/1000 population was 9.6 in Bismarck-type systems whereas in Beveridge-type systems it was 8.8. By 2018, it decreased to 5.6 in Bismarck-type systems (-42%) and to 3.1 in Beveridge-type systems (-65%). In 1980, the average number of hospital beds/1000 population in Eastern Europe was 10.3; in Western Europe, it was 8.5. By 2018, the difference between the 2 regions did not change.

CONCLUSIONS

Although the number of physicians was 33% higher in 1980 in Eastern Europe than in Western Europe, by 2018 the number of physicians was 5% higher in Western Europe. In general, regardless of the healthcare system and geographical location, the proportion of physicians per 1000 population has improved due to a larger decrease in the number of hospital beds.

The Role of Remdesivir in South Africa: Preventing COVID-19 Deaths Through Increasing Intensive Care Unit Capacity

Countries such as South Africa have limited intensive care unit (ICU) capacity to handle the expected number of patients with COVID-19 requiring ICU care. Remdesivir can prevent deaths in countries such as South Africa by decreasing the number of days people spend in ICU, therefore freeing up ICU bed capacity.

Estimating Brazilian states' demands for intensive care unit and clinical hospital beds during the COVID-19 pandemic: development of a predictive model

BACKGROUND

The fragility of healthcare systems worldwide had not been exposed by any pandemic until now. The lack of integrated methods for bed capacity planning compromises the effectiveness of public and private hospitals' services.

OBJECTIVES

To estimate the impact of the COVID-19 pandemic on the provision of intensive care unit and clinical beds for Brazilian states, using an integrated model.

DESIGN AND SETTING

Experimental study applying healthcare informatics to data on COVID-19 cases from the official electronic platform of the Brazilian Ministry of Health.

METHODS

A predictive model based on the historical records of Brazilian states was developed to estimate the need for hospital beds during the COVID-19 pandemic.

RESULTS

The proposed model projected in advance that there was a lack of 22,771 hospital beds for Brazilian states, of which 38.95% were ICU beds, and 61.05% were clinical beds.

CONCLUSIONS

The proposed approach provides valuable information to help hospital managers anticipate actions for improving healthcare system capacity.

Cost-effectiveness of Remdesivir and Dexamethasone for COVID-19 Treatment in South Africa

BACKGROUND

Dexamethasone and remdesivir have the potential to reduce coronavirus disease 2019 (COVID)-related mortality or recovery time, but their cost-effectiveness in countries with limited intensive care resources is unknown.

METHODS

We projected intensive care unit (ICU) needs and capacity from August 2020 to January 2021 using the South African National COVID-19 Epi Model. We assessed the cost-effectiveness of (1) administration of dexamethasone to ventilated patients and remdesivir to nonventilated patients, (2) dexamethasone alone to both nonventilated and ventilated patients, (3) remdesivir to nonventilated patients only, and (4) dexamethasone to ventilated patients only, all relative to a scenario of standard care. We estimated costs from the health care system perspective in 2020 US dollars, deaths averted, and the incremental cost-effectiveness ratios of each scenario.

RESULTS

Remdesivir for nonventilated patients and dexamethasone for ventilated patients was estimated to result in 408 (uncertainty range, 229-1891) deaths averted (assuming no efficacy [uncertainty range, 0%-70%] of remdesivir) compared with standard care and to save $15 million. This result was driven by the efficacy of dexamethasone and the reduction of ICU-time required for patients treated with remdesivir. The scenario of dexamethasone alone for nonventilated and ventilated patients requires an additional $159 000 and averts 689 [uncertainty range, 330-1118] deaths, resulting in $231 per death averted, relative to standard care.

CONCLUSIONS

The use of remdesivir for nonventilated patients and dexamethasone for ventilated patients is likely to be cost-saving compared with standard care by reducing ICU days. Further efforts to improve recovery time with remdesivir and dexamethasone in ICUs could save lives and costs in South Africa.

From intensive care to step-down units: Managing patients throughput in response to COVID-19

QUALITY PROBLEM OR ISSUE

The on-going COVID-19 pandemic may cause the collapse of healthcare systems because of unprecedented hospitalization rates.

INITIAL ASSESSMENT

A total of 8.2 individuals per 1000 inhabitants have been diagnosed with COVID-19 in our province. The hospital predisposed 110 beds for COVID-19 patients: on the day of the local peak, 90% of them were occupied and intensive care unit (ICU) faced unprecedented admission rates, fearing system collapse.

CHOICE OF SOLUTION

Instead of increasing the number of ICU beds, the creation of a step-down unit (SDU) close to the ICU was preferred: the aim was to safely improve the transfer of patients and to relieve ICU from the risk of overload.

IMPLEMENTATION

A nine-bed SDU was created next to the ICU, led by intensivists and ICU nurses, with adequate personal protective equipment, monitoring systems and ventilators for respiratory support when needed. A second six-bed SDU was also created.

EVALUATION

Patients were clinically comparable to those of most reports from Western Countries now available in the literature. ICU never needed supernumerary beds, no patient died in the SDU, and there was no waiting time for ICU admission of critical patients. SDU has been affordable from human resources, safety and economic points of view.

LESSONS LEARNED

COVID-19 is like an enduring mass casualty incident. Solutions tailored on local epidemiology and available resources should be implemented to preserve the efficiency and adaptability of our institutions and provide the adequate sanitary response.

The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals

Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems.

Effects of Cocooning on Coronavirus Disease Rates after Relaxing Social Distancing

As coronavirus disease spreads throughout the United States, policymakers are contemplating reinstatement and relaxation of shelter-in-place orders. By using a model capturing high-risk populations and transmission rates estimated from hospitalization data, we found that postponing relaxation will only delay future disease waves. Cocooning vulnerable populations can prevent overwhelming medical surges.

Impact of Social Distancing Measures on Coronavirus Disease Healthcare Demand, Central Texas, USA

Social distancing orders have been enacted worldwide to slow the coronavirus disease (COVID-19) pandemic, reduce strain on healthcare systems, and prevent deaths. To estimate the impact of the timing and intensity of such measures, we built a mathematical model of COVID-19 transmission that incorporates age-stratified risks and contact patterns and projects numbers of hospitalizations, patients in intensive care units, ventilator needs, and deaths within US cities. Focusing on the Austin metropolitan area of Texas, we found that immediate and extensive social distancing measures were required to ensure that COVID-19 cases did not exceed local hospital capacity by early May 2020. School closures alone hardly changed the epidemic curve. A 2-week delay in implementation was projected to accelerate the timing of peak healthcare needs by 4 weeks and cause a bed shortage in intensive care units. This analysis informed the Stay Home-Work Safe order enacted by Austin on March 24, 2020.

The Impact of Step-Down Unit Care on Patient Outcomes After ICU Discharge

Supplemental Digital Content is available in the text.

Objectives:

To examine whether and how step-down unit admission after ICU discharge affects patient outcomes.

Design:

Retrospective study using an instrumental variable approach to remove potential biases from unobserved differences in illness severity for patients admitted to the step-down unit after ICU discharge.

Setting:

Ten hospitals in an integrated healthcare delivery system in Northern California.

Patients:

Eleven-thousand fifty-eight episodes involving patients who were admitted via emergency departments to a medical service from July 2010 to June 2011, were admitted to the ICU at least once during their hospitalization, and were discharged from the ICU to the step-down unit or the ward.

Interventions:

None.

Measurements and Main Results:

Using congestion in the step-down unit as an instrumental variable, we quantified the impact of step-down unit care in terms of clinical and operational outcomes. On average, for ICU patients with lower illness severity, we found that availability of step-down unit care was associated with an absolute decrease in the likelihood of hospital readmission within 30 days of 3.9% (95% CI, 3.6–4.1%). We did not find statistically significant effects on other outcomes. For ICU patients with higher illness severity, we found that availability of step-down unit care was associated with an absolute decrease in in-hospital mortality of 2.5% (95% CI, 2.3–2.6%), a decrease in remaining hospital length-of-stay of 1.1 days (95% CI, 1.0–1.2 d), and a decrease in the likelihood of ICU readmission within 5 days of 3.6% (95% CI, 3.3–3.8%).

Conclusions:

This study shows that there exists a subset of patients discharged from the ICU who may benefit from care in an step-down unit relative to that in the ward. We found that step-down unit care was associated with statistically significant improvements in patient outcomes especially for high-risk patients. Our results suggest that step-down units can provide effective transitional care for ICU patients.

Assessing Immediate Bed Availability and Barriers to Discharge in a United States Children's Hospital

OBJECTIVES

The aim of this study was to quantify immediate bed availability (IBA) in a United States children's hospital and treatment needs of hospitalized patients whose needs could be met outside a traditional hospital setting.

METHODS

Using a novel tool to capture census, scheduled discharges, and resource needs for hospitalized patients, we surveyed our hospital's 5 non-neonatal inpatient pediatric units on 4 d over 1 y.

RESULTS

Median ward occupancy was 81% (range, 58-79), median intensive care unit occupancy was 80% (range, 7-19), and median IBA was 42% (range, 34-59). A median of 14 patients per day (13% of total capacity) had treatment needs that could be met by providing limited support in a nontraditional setting; the most common reason for requiring ongoing hospitalization in this group of patients was a safe discharge plan.

CONCLUSIONS

Our median IBA of 42% exceeds federal recommendations, but varies widely between days surveyed. Even on days when IBA percentage is high, our total number of available beds is unlikely to meet pediatric population needs in a large-scale public health emergency.

How many beds? Capacity implications of hospital care demand projections in the Irish hospital system, 2015-2030

Existing Irish hospital bed capacity is low by international standards while Ireland also reports the highest inpatient bed occupancy rate across OECD countries. Moreover, strong projected population growth and ageing is expected to increase demand for hospital care substantially by 2030. Reform proposals have suggested that increased investment and access to nonacute care may mitigate some increased demand for hospital care over the next number of years, and it is in this context that the Irish government has committed to increase the supply of public hospital beds by 2600 by 2027. Incorporating assumptions on the rebalancing of care to nonhospital settings, this paper analyses the capacity implications of projected demand for hospital care in Ireland to 2030. This analysis employs the HIPPOCRATES macrosimulation projection model of health care demand and expenditure developed in the ESRI to project public and private hospital bed capacity requirements in Ireland to 2030. We examine 6 alternative projection scenarios that vary assumptions related to population growth and ageing, healthy ageing, unmet demand, hospital occupancy, hospital length of stay, and avoidable hospitalisations. We project an increased need for between 4000 and 6300 beds across public and private hospitals (an increase of between 26.1% and 41.1%), of which 3200 to 5600 will be required in public hospitals. These findings suggest that government plans to increase public hospital capacity over the 10 years to 2027 by 2600 may not be sufficient to meet demand requirements to 2030, even when models of care changes are accounted for.

Distribution of hospital beds in Tehran Province based on Gini coefficient and Lorenz curve from 2010 to 2012

INTRODUCTION

Fair distribution of hospital beds across various regions is a controversial subject. Resource allocation in health systems rarely has focused on those who need it most and, in addition, is often influenced by political interests. The study assesses the distribution of hospital beds in different regions in Tehran, Iran, during 2010-2012.

METHODS

This cross-sectional study was conducted in all regions of Tehran (22 regions) during 2010 to 2012. All hospital beds in these regions are included in the study. Data regarding populations of each region were obtained from the Statistics Center of Iran. According to the data, the total number of beds (N.B) and population (P) in 2010 (N.B=19075, P= 7585000), 2011 (N.B=21632, P= 9860500), and 2012 (N.B=21808, P=12818650). The instrument was a form, including the name of the hospital, the district in which the hospital was located, the number of staffed beds, the name of each region, and its population. Data analysis was performed using DASP software version 2.3.

RESULTS

The results demonstrate that the Gini coefficient of distributed beds in 22 regions of Tehran was 0.46 in all three years and specifically calculated 0.4666 in 2010, 0.4658 in 2011 and 0.4652 in 2012. The Gini coefficient of beds in 22 regions of Tehran is not fair in comparison with the population of each region during the years 2010 to 2012.

CONCLUSION

The results demonstrate that the distribution of beds in regions in Tehran is not fair in relation to the population of each region-and some regions had no hospitals. Therefore, it is essential for policymakers to frequently monitor this issue and investigate the fair distribution of hospital beds.

Pediatric Disposition Classification (Reverse Triage) System to Create Surge Capacity

BACKGROUND

Critically insufficient pediatric hospital capacity may develop during a disaster or surge event. Research is lacking on the creation of pediatric surge capacity. A system of "reverse triage," with early discharge of hospitalized patients, has been developed for adults and shows great potential but is unexplored in pediatrics.

METHODS

We conducted an evidence-based modified-Delphi consensus process with 25 expert panelists to derive a disposition classification system for pediatric inpatients on the basis of risk tolerance for a consequential medical event (CME). For potential validation, critical interventions (CIs) were derived and ranked by using a Likert scale to indicate CME risk should the CI be withdrawn or withheld for early disposition.

RESULTS

Panelists unanimously agreed on a 5-category risk-based disposition classification system. The panelists established upper limit (mean) CME risk for each category as <2% (interquartile range [IQR]: 1-2%); 7% (5-10%), 18% (10-20%), 46% (20-65%), and 72% (50-90%), respectively. Panelists identified 25 CIs with varying degrees of CME likelihood if withdrawn or withheld. Of these, 40% were ranked high risk (Likert scale mean ≥7) and 32% were ranked modest risk (≤3).

CONCLUSIONS

The classification system has potential for an ethically acceptable risk-based taxonomy for pediatric inpatient reverse triage, including identification of those deemed safe for early discharge during surge events.

A Successful Model for a Comprehensive Patient Flow Management Center at an Academic Health System

This article reports on an innovative approach to managing patient flow at a multicampus academic health system, integrating multiple services into a single, centralized Patient Flow Management Center that manages supply and demand for inpatient services across the system. Control of bed management was centralized across 3 campuses and key services were integrated, including bed management, case management, environmental services, patient transport, ambulance and helicopter dispatch, and transfer center. A single technology platform was introduced, as was providing round-the-clock patient placement by critical care nurses, and adding medical directors. Daily bed meetings with nurse managers and charge nurses drive action plans. This article reports immediate improvements in the first year of operations in emergency department walkouts, emergency department boarding, ambulance diversion, growth in transfer volume, reduction in lost transfers, reduction in time to bed assignment, and bed turnover time. The authors believe theirs is the first institution to integrate services and centralize bed management so comprehensively.

Addressing inpatient crowding by smoothing occupancy at children's hospitals

OBJECTIVE

To quantify the difference in weekday versus weekend occupancy, and the opportunity to smooth inpatient occupancy to reduce crowding at children's hospitals.

METHODS

Daily inpatient census data for 39 freestanding, tertiary-care children's hospitals were used to calculate occupancy and to model the impact of reducing variation in occupancy and the change in the number of patients, patient-days, and hospitals exposed to high occupancy pre- and post-smoothing. We also calculated the proportion of weekly admissions that would require different scheduling to achieve within-week smoothing.

RESULTS

Overall, hospitals' mean occupancy ranged from 70.9% to 108.1% on weekdays, and 65.7% to 94.9% on weekends. Weekday occupancy exceeded weekend occupancy with a median difference of 8.2% points. The mean post-smoothing reduction in weekly maximum occupancy across all hospitals was 6.6% points. Through smoothing, 39,607 patients from the 39 hospitals were removed from exposure to occupancy levels >95%. To achieve within-week smoothing, a median 2.6% of admissions would have to be scheduled on a different day of the week; this equates to a median of 7.4 patients per week (range: 2.3-14.4).

CONCLUSION

Hospitals do have substantial unused capacity, and smoothing occupancy over the course of a week could be a useful strategy that hospitals can use to reduce crowding and protect patients from crowded conditions.

Children's hospitals do not acutely respond to high occupancy

OBJECTIVE

High hospital occupancy may lead to overcrowding in emergency departments and inpatient units, having an adverse impact on patient care. It is not known how children's hospitals acutely respond to high occupancy. The objective of this study was to describe the frequency, direction, and magnitude of children's hospitals' acute responses to high occupancy.

METHODS

Patients who were discharged from 39 children's hospitals that participated in the Pediatric Health Information System database during 2006 were eligible. Midnight census data were used to construct occupancy levels. Acute response to high occupancy was measured by 8 variables, including changes in hospital admissions (4 measures), transfers (2 measures), and length of stay (2 measures).

RESULTS

Hospitals were frequently at high occupancy, with 28% of midnights at 85% to 94% occupancy and 42% of midnights at > or =95% occupancy. Whereas half of children's hospitals used occupancy-mitigating responses, there was variability in responses and magnitudes were small. When occupancy was >95%, no more than 8% of hospitals took steps to reduce admissions, 13% increased transfers out, and up to 58% reduced standardized length of stay. Two-day lag response was more common but remained of too small a magnitude to make a difference in hospital crowding. Additional modeling techniques also revealed little response.

CONCLUSIONS

We found a low rate of acute response to high occupancy. When there was a response, the magnitude was small.

Clinical review: Emergency department overcrowding and the potential impact on the critically ill

Critical care constitutes a significant and growing proportion of the practice of emergency medicine. Emergency department (ED) overcrowding in the USA represents an emerging threat to patient safety and could have a significant impact on the critically ill. This review describes the causes and effects of ED overcrowding; explores the potential impact that ED overcrowding has on care of the critically ill ED patient; and identifies possible solutions, focusing on ED based critical care.

Medical care capacity for influenza outbreaks, Los Angeles

In December 1997, media reported hospital overcrowding and "the worst [flu epidemic] in the past two decades" in Los Angeles County (LAC). We found that rates of pneumonia and influenza deaths, hospitalizations, and claims were substantially higher for the 1997-98 influenza season than the previous six seasons. Hours of emergency medical services (EMS) diversion (when emergency departments could not receive incoming patients) peaked during the influenza seasons studied; the number of EMS diversion hours per season also increased during the seasons 1993-94 to 1997-98, suggesting a decrease in medical care capacity during influenza seasons. Over the seven influenza seasons studied, the number of licensed beds decreased 12%, while the LAC population increased 5%. Our findings suggest that the capacity of health-care systems to handle patient visits during influenza seasons is diminishing.